The papers in this special section examine the use of predictive intelligence for bioinformatics. Big data is fueling diverse research directions in both medical image analysis and computer vision research fields. These can be divided into two main categories: (1) analytical methods, and (2) predictive methods. While analytical methods aim to efficiently analyze, represent, and interpret data, predictive methods leverage the data currently available to predict observations at present (e.g., by completingmissing observations), at previous time-points (e.g., by solving reverse problems), or at later time-points (i.e., forecasting the future). For instance, a method which only focuses on classifying patients with mild cognitive impairment (MCI) and patients with Alzheimer’s disease (AD) is an analytical method, while a method that predicts if a subject diagnosed with MCI will remain stable or convert to AD over time is a predictive method. Similar cases can be established for various neurodegenerative or neuropsychiatric disorders, degenerative arthritis, or cancer studies, in which the disease/disorder develops over time.

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社论：生物医学和健康信息学中的预测情报

本节中的论文探讨了预测情报在生物信息学中的应用。大数据推动了医学图像分析和计算机视觉研究领域的各种研究方向。这些可以分为两大类：（1）分析方法和（2）预测方法。分析方法旨在有效地分析，表示和解释数据，而预测方法则利用当前可用于预测当前观测结果的数据（例如，通过完成缺失观测值），在先前时间点（例如，通过解决反向问题）或稍后的时间点（即预测未来）。例如，一种仅侧重于轻度认知障碍（MCI）和阿尔茨海默氏病（AD）患者的方法就是一种分析方法，而预测被诊断为MCI的受试者会随着时间的推移保持稳定或转变为AD的方法是一种预测方法。可以为各种神经退行性疾病或神经精神疾病，退行性关节炎或癌症研究建立类似的案例，这些疾病/疾病会随着时间而发展。